Frameworks: Garden: A FAIR Framework for Publishing and Applying AI Models for Translational Research in Science, Engineering, Education, and Industry

框架：Garden：用于发布和应用人工智能模型进行科学、工程、教育和工业转化研究的公平框架

• 批准号: 2209892
• 负责人: Ian Foster
• 金额: $ 349.65万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2209892&HistoricalAwards=false
• 关键词: Frameworks; Garden; FAIR; Framework; Publishing

Harnessing powerful new advances in machine learning (ML) and artificial intelligence (AI) is key to 1) maintaining and building national competitiveness in the sciences and engineering, 2) realizing breakthroughs in health and medicine, 3) enabling the creation of industries of the future, and 4) increasing economic growth and opportunity. Today, researchers are achieving exciting results with these new ML/AI methods in applications ranging from materials discovery, chemistry, and drug discovery to high energy physics, weather prediction, advanced manufacturing, and health. Yet, much work remains. These new methods and results are not easily applied by others due to the specialized expertise and resources needed to understand, develop, share, adapt, test, deploy, and run the resulting ML/AI models. To overcome these barriers to progress, this project seeks to develop methods and tools for constructing and creating Model Gardens, collections of curated and tested ML/AI models linked with the data and computing resources required to advance the work of a specific research community. Such new methods, software, and tools can make it simple for model producers to publish models in forms that are easily consumed by others, and for model consumers to discover published models and integrate them into their applications in academia or industry. The project connects researchers in materials science, physics, and chemistry enabling the establishment of Model Gardens for their communities and empowering key research centers to collect and provide broad access to new methods and models resulting from their work. Further, the project facilitates the connection of aspiring researchers with scientific problems, engaging hundreds of students from diverse backgrounds (including rural community college partners) in learning and contributing to software development, model publication, development of new AI/ML applications, and training of a next-generation ML/AI-empowered workforce through hosted workshops, open office hours, and development of a new engagement platform.This project overcomes the barriers to the dissemination and application of new ML/AI methods by creating a new CSSI framework—the Garden Framework to support the construction and operation of Model Gardens: collections of curated models linked with the data and computing resources required to advance the work of specific communities. By reducing the friction associated with model publication, discovery, access, and deployment; providing for the disciplined and structured organization and linking of data, models, and code; associating appropriate metadata with models to promote reuse and discoverability, and applying quality assessment measures (e.g., automated testing, uncertainty quantification) to support model comparison; supporting the development of communities around specific model classes and research challenges; and permitting easy access to models without (and with) download and installation, established Model Gardens reduce barriers to the use of ML/AI methods and promote the nucleation of communities around specific datasets, methods, and models.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

利用机器学习（ML）和人工智能（AI）的强大新进展是1）保持和建立科学和工程国家竞争力，2）实现健康和医学突破，3）创造未来产业，4）增加经济增长和机会的关键。今天，研究人员正在利用这些新的ML/AI方法在从材料发现、化学和药物发现到高能物理、天气预测、先进制造和健康等应用中取得令人兴奋的成果。然而，还有许多工作要做。这些新方法和结果不容易被其他人应用，因为需要专业知识和资源来理解，开发，共享，调整，测试，部署和运行最终的ML/AI模型。为了克服这些进展障碍，该项目旨在开发用于构建和创建模型花园的方法和工具，这些模型花园是与推进特定研究社区工作所需的数据和计算资源相关联的策划和测试ML/AI模型的集合。这些新的方法、软件和工具可以使模型生产者以其他人容易使用的形式发布模型变得简单，也可以使模型消费者发现已发布的模型并将其集成到学术界或工业界的应用程序中。该项目将材料科学、物理学和化学领域的研究人员联系起来，为他们的社区建立模型花园，并授权主要研究中心收集和提供广泛的新方法和模型。此外，该项目促进了有抱负的研究人员与科学问题的联系，吸引了数百名来自不同背景的学生（包括农村社区学院合作伙伴）通过举办研讨会，开放办公时间，该项目通过创建一个新的CSSI框架-花园框架来支持模型花园的建设和运营，从而克服了新ML/AI方法的传播和应用障碍：与推进特定社区工作所需的数据和计算资源相关联的精选模型集合。通过减少与模型发布、发现、访问和部署相关的摩擦;提供数据、模型和代码的规范化和结构化组织和链接;将适当的元数据与模型相关联以促进重用和可重用性，并应用质量评估措施（例如，自动化测试，不确定性量化），以支持模型比较;支持围绕特定模型类别和研究挑战的社区发展;并允许轻松访问模型，（以及）下载和安装，建立的模型花园减少了使用ML/AI方法的障碍，并促进了围绕特定数据集，方法，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。